Want to keep tabs on your customers? Big Data can help

Even when Wojciech Gryc was a polymath student of mathematics, international relations and computer science, he noticed something about the complicated mathematical models he was building, meant to predict how a company's customers would behave.

This kind of modeling work is both expensive and lucrative: Many companies collect data about their customers and sales, but big firms can afford to hire teams of consultants to break down that data to deliver insights into what makes those customers tick. Even after launching a career at a consultancy that supplies these services, Mr. Gryc kept thinking that there was an economy of scale that could be had.

"The first time you built it, you realize, oh wait, this is very cool, this is interesting," he says. "The next few times you're doing more or less the same thing you were doing before."

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So he got to thinking: Not every client needs a custom model, built from the ground up. This thinking led Mr. Gryc take the leap from his job to launch a new venture, a company called Canopy Labs, that's aiming to turn the kind of big data analysis that this emergent field into a service that's available to companies large and small, on the kind of automated, self-serve basis that consumers have come to expect.

The company's products are approaching private beta, but the process will be simple, says Mr. Gryc: "You upload your customer data, you get a model back." It is, in a nutshell, analytics-as-a-service.

Canopy Labs is just one example of the ways that "Big Data" applications are finding their way from the corporate world into the small business space. Big Data is a term that is used to describe the mountains of data that information systems churn out in the course of day-to-day life – from the records kept by a point-of-sale terminal to the logs kept by automatic doors – and the insights that can be gleaned by mining that data.

By default, Big Data is the province of those who stockpile the data, and have the means to process it. But the rapidly shrinking cost of both computing power and storage capacity is bringing this data into the grasp of entrepreneurs who have the ingenuity to process it and from there, into the hands of smaller clients who will be able to leverage these services.

For instance, Canopy Labs' product takes customer data and will provide several insights in return. Segmentation analyses can break down how customers clump together demographically, determining which groups of customers are attracted to which products or services. It can also help estimate a customer's potential lifetime value to a company, based on past behaviour.

The service can also generate what Mr. Gryc terms "propensity models," which determine the probability of a customer taking a course of action, based on an analysis of their previous actions.

This is a point where Big Data departs from more traditional forms of rote data collection: Where a simpler approach might have involved aggressively canvassing customers to collect personal data for a database, Big Data approaches look to suss information out of other, existing indications.

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For example, a customer who purchases bridal magazines and invitation stationary might have done so as a gag, but the odds are they got married – which can tell the merchant something about the kinds of purchases they'll want to make in the future.

"A company will never know you got married; they're just have an estimation of the probability you got married," says Mr. Gryc. But the sum of those estimations and probabilities can paint a telling picture about who a company's clients are, and what they want.

Collecting data's never easy; different companies store their information in different ways and dealing with messy datasets is something that Canopy Labs says it is prepared to deal with. And Mr. Gryc says his aim isn't to solve the entire problem of analytics for small business, but rather just the sales and marketing angle. Still, he's wagering that small business will be willing to pay a subscription to have their data routinely vetted by software, rather than consultants.

"It's about getting an answer quickly, rather than giving you the academic-quality journal answer."

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